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Insights & perspectives

Shaping the future of work and competence as a system.

Through articles, analyses, and metacognitive reflections, Erigo explores competence as a structural driver for organizations and society. The texts span from practical insights to systemic perspectives, creating a space where knowledge becomes both measurable and meaningful.

The Confirmation Machine

The Confirmation Machine

Synthetic safety, the interpretive gap and the MIT paper that confirms

For nearly two decades, I have worked with how digital systems affect human behaviour and cognition. When language models entered the market, I began systematically crossing neuroscience, behavioural research and LLM architecture to understand what actually happens when people converse with AI.
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We chase AI's capabilities

We chase AI's capabilities

What it does to ours deserves equal attention

We track every new model release. We benchmark, compare, implement. An entire industry is built around measuring what AI can do. The question I have followed is a different one: what happens to our own capacity in the process? I have worked with behavioral data in digital environments for nearly two decades. Since language models entered the market, I have continuously tracked how this new layer affects what we already knew about algorithmic flows, concentration, and interpretive ability.
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Understanding the linguistic mind

Understanding the linguistic mind

A prelude to machine syntax

Language builds the framework of thought. Every structure we use to speak also becomes a structure we use to think. Across languages, these structures differ in grammar, metaphor and category, creating distinct ways of perceiving and relating to the world.
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Cognitive Integrity

Cognitive Integrity

The missing layer of developer ethics

A developer builds an AI assistant for financial compliance. At first, the system works smoothly. It cites regulations, answers questions, and feels reliable.
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The hidden weight of alignment

The hidden weight of alignment

How reinforcement shapes truth in LLMs

You ask a model about a sensitive issue. Instead of answering, it politely refuses. That refusal is not random. It is alignment in action, the hidden layer that decides what a large language model (LLM) is allowed to say.
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Who owns the truth in Large Language Models?

Who owns the truth in Large Language Models?

Large language models are rapidly becoming the interfaces through which knowledge is accessed, shaped, and distributed. If only a handful of companies own these models, they also hold the power to define what appears as truth in our digital discourse.
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Bias in AI

Bias in AI

Weightings that shape our worldview

We are galloping in step with technology. Fascination carries us forward. But there are questions we rarely pause to consider. Bias in AI is often discussed as a technical problem — something that can be adjusted, coded away, fine-tuned. But bias is not just about data. It is about which interpretations are given priority. Which worldview is reinforced. Which values are amplified, and which are cut away.
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What is Truth in the LLM Era

What is Truth in the LLM Era

And how to use Language Models without losing competence

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Synthetic safety and AI

Synthetic safety and AI

When confirmation replaces inquiry

Generative AI gives us answers quickly, politely, and often with a high degree of familiarity. But what happens when our brain interprets this as confirmation, and when confirmation begins to replace inquiry? As more people turn to AI instead of human dialogue, the conditions for how we train empathy, develop judgment, and build knowledge are changing. The sense of safety that emerges is not necessarily wrong, but it is synthetic.
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